SmartBAN With Periodic Monitoring Traffic: A Performance Study on Low Delay and High Energy Efficiency

Machine-Learning-Based Uplink Throughput Prediction from Physical Layer Measurements

The uplink (UL) throughput prediction is indispensable for a sustainable and reliable cellular network due to the enormous amounts of mobile data used by interconnecting devices, cloud services, and social media. Therefore, network service providers implement highly complex mobile network systems with a large number of parameters and feature add-ons. In addition to the increased complexity, old-fashioned methods have become insufficient for network management, requiring an autonomous calibration to minimize utilization of the system parameter and the processing time. Many machine learning algorithms utilize the Long-Term Evolution (LTE) parameters for channel throughput prediction, mainly in favor of downlink (DL). However, these algorithms have not achieved the desired results because UL traffic prediction has become more critical due to the channel asymmetry in favor of DL throughput closing rapidly. The environment (urban, suburban, rural areas) affect should also be taken into account to improve the accuracy of the machine learning algorithm. Thus, in this research, we propose a machine learning-based UL data rate prediction solution by comparing several machine learning algorithms for three locations (Houston, Texas, Melbourne, Florida, and Batman, Turkey) and determine the best accuracy among all. We first performed an extensive LTE data collection in proposed locations and determined the LTE lower layer parameters correlated with UL throughput. The selected LTE parameters, which are highly correlated with UL throughput (RSRP, RSRQ, and SNR), are trained in five different learning algorithms for estimating UL data rates. The results show that decision tree and k-nearest neighbor algorithms outperform the other algorithms at throughput estimation. The prediction accuracy with the R2 determination coefficient of 92%, 85%, and 69% is obtained from Melbourne, Florida, Batman, Turkey, and Houston, Texas, respectively.

Integrated Performance Evaluation of the Smart Body Area Networks Physical Layer for Future Medical and Healthcare IoT

This paper performs integrated performance evaluation, including preamble detection in the Smart Body Area Networks (SmartBAN) physical layer (PHY). The system specifications for a PHY and media access control layer (MAC) in SmartBAN, which is a standard for medical and health care advanced by the European Telecommunications Standards Institute (ETSI), were issued in April 2015. In the PHY, the packet structure has a two-octet preamble, which is used, e.g., for timing synchronization. However, it is considered that the current preamble structure is not appropriate for handling medical and healthcare data that are required to have high reliability because of the too simple structure. Therefore, we propose adding a start frame delimiter (SFD) to correctly detect the header position. Computer simulations indicate that preambles with an SFD consisting of an orthogonal maximal length sequence (M-sequence) perform better than SmartBAN and similar approaches, particularly when transmitting over the IEEE model CM3. In addition, the packet error ratio (PER) and energy efficiency are evaluated in an integrated manner while taking preamble detection into consideration. The numerical results from computer simulations indicated the best performance with respect to PER was achieved using a preamble with orthogonal M-sequences of 4 octets. However, for energy efficiency, better results were obtained using a preamble with orthogonal M-sequences of 2 octets. Additionally, the theoretical analysis found the optimum length of the PHY packet to achieve the maximum energy efficiency with PER less than 10⁻².